Structuring of materials at the micro-/nano-scale has become a standard method for creating optical, thermal, acoustic, and mechanical properties, which are not possible by traditional stochastic material processing. The new materials created by the deliberate control of their structures have been called ?metamaterials? and will play a critical role in developing future high-performance materials. The course aims to provide multidisciplinary knowledge for understanding metamaterials with selected applications.

In this course, we will study the major concepts, challenges, and solution strategies to engineer the logistics of the supply chain. We will focus on the modeling and rigorous analysis of problems related to the efficient design and operation of the supply chain. Topics include facility location, routing and transportation, inventory management, and supply chain strategies. We will use various techniques to solve these problems, such as mixed-integer programming, dynamic programming, non-linear optimization, and game theory. Both deterministic and stochastic scenarios will be considered.

Review of engineering principles of solar energy conversion including collection techniques, thermal and direct conversion, system performance prediction, and selected topics. Prerequisite: M&I-Eng 354.

Energy sources and power systems used by industry and utilities to produce electricity, mechanical power, process heating and cooling are examined for energy efficiency and economic feasibility. Analysis and design of thermal systems and specific components are considered. Prerequisites: M&I-Eng 340 and 354.

Introduction to automated vehicle systems with emphasis on transportation safety. Topics include historical background, advanced technologies in sensors and control, human factors design and application, research methodologies and state of the science, and policy and regulation.

Dynamic or sequential decision-making tackles a type of problems where decisions need to change over time to adapt to changing environments, occurring as a consequence of natural environmental dynamics and/or influence of prior decisions. These types of problems are encountered in a variety of fields.

This course will cover statistical methods now widely used in data analysis, learning and prediction such as regression and classification techniques, feature selection, decision trees, and unsupervised learning methods such as clustering and principal components analysis. The emphasis will be on applying the statistical methods to data sets and understanding the optimization theory that drives these methods.

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